When capturing an image (either a still image or video image) using an imaging device, the captured image can blur if the imaging device, the object being imaged, or both, are in motion. The motions of the imaging device and the object being imaged may be composed of many components over a wide frequency range. For example, relatively high-frequency vibration from mechanical systems, to relatively lower frequency motion such as relative geometric motion between the imaging device and the object being imaged.
Image stabilization techniques have been developed to eliminate, or at least reduce, image blurring associated with such motions. Current real-time image stabilization techniques fall generally into two classifications, optical image stabilization and digital image stabilization. Optical image stabilization is the preferred approach for high performance systems because of its high image quality. Current optical image stabilization techniques use rate sensors to detect and correct disturbances to the imaging system, but are not configured to detect and compensate for any motion of the object itself. Thus, image blurring may still occur when the object being imaged is moving. Digital image stabilization techniques typically implement digital transformations on portions of the image itself. These techniques can reduce vibrations from video images or improve still image quality, but can increase noise in the captured images. Current image stabilization techniques may also rely on radar or laser tracking devices to provide object designation and tracking functions. These tracking devices can be relatively complex, heavy, and costly.
This is a problem when attempting to track and image moving objects such as missiles, aircraft, spacecraft, planetary surfaces from orbiting spacecraft, and astronomical bodies, or more terrestrial uses such as sporting events photography and law enforcement imaging. It is equally a problem when the imaging system itself is moving and is tracking and imaging other stationary or moving objects.
The above-described issues can be exacerbated for imaging applications that rely on a filter. Some example applications include polarizing, RGB, photometric, UV, IR, and narrow band filtered imaging. When stabilization and/or tracking is used with filtered imaging, the light intensity falling on the stabilization and tracking detector(s) may be reduced. This is because the stabilization and tracking detector(s) may share the filtered illuminated field with the primary imaging detector. This can cause reduced signal-to-noise ratio and degraded stabilization and tracking performance. To try and alleviate this issue, some systems may include a separate tracking optic and imager, which can increase overall system complexity and cost.
Hence, there is a need for an image stabilization and tracking system, for both filtered and unfiltered imaging applications, that does not rely on relatively complex, heavy, and/or expensive tracking devices and/or separate tracking optics and imagers. There is also a need for an image stabilization and tracking system that compensates for all motion components simultaneously at most (if not all) of the frequencies for both the imaging device and object being imaged. The present invention addresses at least these needs.